Manajemen | Fakultas Ekonomi Universitas Maritim Raja Ali Haji joeb.83.1.37-44
Journal of Education for Business
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Building Skills in Thinking: Toward a Pedagogy in
Metathinking
Victoria Crittenden & Arch G. Woodside
To cite this article: Victoria Crittenden & Arch G. Woodside (2007) Building Skills in Thinking:
Toward a Pedagogy in Metathinking, Journal of Education for Business, 83:1, 37-44, DOI:
10.3200/JOEB.83.1.37-44
To link to this article: http://dx.doi.org/10.3200/JOEB.83.1.37-44
Published online: 07 Aug 2010.
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BuildingSkillsinThinking:Towarda
PedagogyinMetathinking
VICTORIACRITTENDEN
ARCHG.WOODSIDE
BOSTONCOLLEGE
CHESTNUTHILL,MASSACHUSETTS
ABSTRACT.Mostmanagersdonot
receiveformaltraininginmetathinking—
thatis,theyarenottrainedformallyin
thinkingaboutthinkingorinthinkingabout
deciding.Inthisarticle,theauthorsreview
thelackofeducationalfocusonmetathinkingandsuggestseveraltoolsforimproving
thedecision-makingprocessandforskill
buildinginmetathinking.Thetoolsinclude
twoexperientialexercisesthatfacilitate
learninginmetathinking.
Keywords:decisionanalysis,learning
theory,metathinking,pedagogy
Copyright©2007HeldrefPublications
T
he way chief executive officers
(CEOs) draw conclusions and
makedecisionscanhavedisastrousconsequences. For example, government
CEOs GeorgeW. Bush (United States),
Tony Blair (United Kingdom), John
Howard(Australia),andadditionalcoalition country CEOs concluded in early
2003 that Iraqi leaders had weapons
of mass destruction and were refusing
to disarm these weapons. Thus, these
governmentCEOsmadethedecisionto
declarewaronIraq.Inmid-2004(more
thanayearafterthewarwasdeclaredas
officiallywon),theCEOshadfoundno
weapons of mass destruction, close-tofull-blown civil war was raging in Iraq,
andworldterrorismhadincreased.Amid
all of this, the U.S. CEO reported that
therewereweaponsofmassdestruction.
At a more mundane level, consider
whyhighlysuccessfulfirmssuchasPolaroid and Lucent became failures. The
coverstoryoftheMay27,2002issueof
Fortunemagazinedescribed10bigmistakes as the primary reasons why companiesfail,withacriticalmistakebeing
that people presume that the future will
begoodonthebasisofhistoricalsuccesses rather than planning for unexpected
changes(Charan,Useem,&Harrington,
2002).WeickandSutcliffe(2001)made
thesameobservationintheiraptlytitled
monograph,ManagingtheUnexpected.
These examples point out the obvious:thatCEOs,middlelevelmanagers,
andtherestofusarepronetodrawing
inaccurateconclusionsandmakingbad
decisions. It is unfortunate that such
thinking (a) occurs frequently, (b) can
be very expensive, (c) often wrecks
localeconomies,and(d)cancausemassive layoffs, terror, or even death (for
reviewsonthispoint,seeBaron,2000;
Bazerman,1998;Gilovich,1991).Rather than shaking our heads or pointing
fingersatsomeoneelse’sbaddecisions,
peopleshouldidentifytoolstoimprove
the accuracy and quality of decisions
(cf.Martz&Shepherd,2003).Inother
words, what tools will help decision
makersbecomemorecognizantoftheir
decisionprocessesandoutcomes?With
the high frequency and seriousness of
baddecisionmaking,thecreation,testing, and teaching of tools to improve
thinking (i.e., increasing sensemaking
quality in becoming aware, acquiring
knowledge,interpretingdataandinformation,drawingconclusions,deciding,
and evaluating) are important in the
businessschoolclassroom(cf.Shadish,
Cook,&Levitton,1991;Weick,1995).
MarchandOlsen(1976)observed,
“Individuals and organizations make
sense of their experiences and modify behavior in terms of their interpretations” (p. 56). Weick (1995)
referred to this as sensemaking, and
he described sensemaking as “how
peoplegeneratethatwhichtheyinterpret”(p.13).
September/October2007
37
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Parry (2003) suggested that sensemakingisaprocessinwhichindividualsandgroupsinorganizationsorganize
their experiences about reality. As an
example,considerthequestion,“What’s
reallyhappening?”Thisquestiongenerallycontainsfoursubissues:
1.What actions being done now help
improvetheorganization’sperformance?
2.What actions are wasted motions
(i.e.,whatactionsarewetakingthatdo
notcontributebutdowasteourtime)?
3.What actions harm the organization’s performance (i.e., what actions
are counterproductive in the organization’sachievementofwhatreallyneeds
tobeaccomplished)?
4.Whatactionsarewenotdoingnow
butshouldwebedoingtoimprovethe
organization’sperformance?
However,afifthsubissuethattends
to be overlooked in this process is
more important. How does one go
abouttheprocessoffindingoutwhat
isreallyhappening?Animplicitmental model that decision makers often
useisoneinwhichthepersonbelieves
thatwhatcomestomindfirstisaccurate(Senge,1990).
Itseemsthattheprocessofinterpretation
is so reflexive and immediate that we
often overlook it. This, combined with
the widespread assumption that there is
butoneobjectivereality,iswhatmaylead
people to overlook the possibility that
others may be responding to a very different situation. (Gilovich, 1991, p. 117;
cf.Surowiecki,2004)
Thisfifthsubissuerequiresindividualstoengageinmetathinking.Leffand
Nevin(1990)describedmetathinkingas
thinkingandcreatingstrategiestoassist
one’sthinking.
In this article, we advocate that faculty,students,andexecutivesadoptthe
view that all decision makers need to
learn and practice metathinking skills.
Although social psychologists spend
considerable time and energy studyingthinkingprocesses,businessschool
academicianshavenotdoneathorough
joboftranslatingtheresearchintoclassroom experiences that engage students
inunderstandingthelinkbetweenmetathinkingandbusinesssuccess.
In the next section, we review severalkeystudiesonandtoolsformeta38
JournalofEducationforBusiness
thinkinginrelationtodecisionquality.
Then, we suggest a simple classroom
example that engages students in the
thinking-about-thinkingtopic.Weprovidetworigorousexperientialexercise
examples that are applicable in the
businessschoolclassroom.Inthenext
section, we discuss classroom use of
the exercises and offer a brief list of
recommended readings. We conclude
withalookatthedifferencesbetween
scientificandexecutivethinking.
ImprovingtheQualityof
Decisions
Traditionally, educators have not
included formal training in metathinking in the core (or even elective) business education curriculum. Few academic programs, including those in
executiveeducation,includecoursesin
thinking about thinking or in thinking
about deciding. Therefore, few businessstudentsparticipateincoursesthat
focusonacquiringprescriptivetoolsfor
improving the quality of thinking and
deciding(e.g.,Sterman,2001).
Two reasons may be responsible for
thislackofeducationalfocusandtraining.First,overconfidencebiasiswidespread: Most executives tend to rely
toooftenontheirunconsciouslydriven
automatic thoughts (Bargh, Gollwitzer,
Lee-Chai, Barndollar, & Troetschel,
2001; Wegner, 2002). There is a natural tendency to assume that intuitive
beliefsareaccurateandthatrelyingon
external heuristics (e.g., written checklists,explicitprotocols)isunnecessary.
People’s initial response is often one
ofdisbeliefandresentment,evenwhen
presented with hard evidence that formalexternalsearchingofrelevantinformation sources and the use of explicit
decision rules result in more accurate
decisions than does intuitive judgment
alone.Suchresentmentisattributableto
animplicitlossofauthoritytoevaluate
anddecide(e.g.,Gaither,2002).Second,
researchinthearea,asascientificfield
of study, is relatively new. Researchers recognized metathinking—unlike
related fields of study (e.g., biology,
sociology, psychology)—as a field of
formalresearchonlyrecently(e.g.,see
the landmark works by Baron, 2000;
Shadishetal.,1991;Weick,1995).
ToolsforImprovingThinking
Quality
Regarding the quality of decisions,
Gilovich(1991)stated,
Afundamentaldifficultywitheffective
policy evaluation is that we rarely get
to observe what would have happened
if the policy had not been put into
effect. Policies are not implemented as
controlled experiments, but as concerted actions. Not knowing what would
havehappenedunderadifferentpolicy
makes it enormously difficult to distinguish positive or negative outcomes
fromgoodorbadstrategies.Ifthebase
rate of success is high, even a dubious
strategycanbeseenaswise;ifthebase
rateislow,eventhewiseststrategycan
seemfoolish.(pp.41–42)
Severalusefultoolsarenowavailable
forimprovingsensemakingcapabilities
(e.g., Baron, 2000; Gigerenzer, 2000;
Green, 2002; Green & Armstrong,
2004).These tools include (a) estimatingwhatwouldhappenifapolicyhad
notbeenputintoeffect(e.g.,Campbell,
1969) and (b) software programs that
help structure problems and test the
impactofalternativeproblemstructures
(e.g.,Clemen&Reilly,2001).
Training in metathinking may help
overcome fundamental attribution error.
Fundamental attribution error is the
tendency of a person to blame other
peopleorenvironmentalforcesforabad
decisionratherthanrecognizingthatthe
processthatthepersonappliedtoreacha
decision reflects shallow systems thinking (Plous, 1993). “When we attribute
behavior to people rather than system
structure, the focus of management
becomes scapegoating rather than the
design of organizations [and problemsolving procedures] in which ordinary
peoplecanachieveextraordinaryresults”
(Sterman,2001,p.17).
The introduction and application of
variousmetathinkingtoolsintothetraditional school of management classroom may be difficult for educators to
implement,particularlygiventhefunctional nature of most business school
curricula. However, there is one metathinkingprocess,experientialexercises,
that fits clearly within the school of
thought referred to as constructivism
(Garman,1997;Martz,Neil,&Biscaccianti, 2003). Constructivism theories
suggestthatactiveparticipationisbene-
ficialinthelearningprocess.Inanexperientialexercise,learnerscanconstruct
a world by combining past informationwithfuture-orienteddispositionsto
actively engage in the learning process
(cf.Kolb,1984).
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Classroom-Oriented,Experiential
ExercisesinMetathinking
Thinking about how one thinks is
not an easy topic of discussion among
students,regardlessofwhethertheyare
undergraduates, graduates, or executives.Bynature,thetopicof“thinking”
is vague and hard to grasp. Davenport
(2004)suggestedabasic,yetprovoking,
exerciseforengagingpeopleinthinking
abouthowtheythink:
2+2=_____
Howdoyouarriveattheanswer?
A.Purelyfrommemory
B.Analysisofthenumbers
C.Counting
D.Avisualprocess
A natural response is, “Well, I just
know that!” This drives home the primary question, “How do you know
that?”Althoughsimplistic,thisexercise
engagesparticipantseasilyandactively
inadiscussionabouthowbusinesspeople address problems. In this scenario,
participants may recall kindergarten
classes with four of the same items on
the board, envision flash cards, recall
counting on fingers, or recall problem
recitation. Regardless of the solution
method,theexerciseforcesparticipants
to become aware of their individual
thinking processes. This awareness is
thefirststepinmetacognition.
This simple exercise and subsequent discussion engage students in
the metathinking process.This may be
the first time they—as business school
students—have talked about thinking,
althoughsomeoftheirsyllabi(particularly those in case-based courses) may
havereferredtocriticalthinkingskills.
Yet, becoming aware of one’s individual knowledge, assumptions, skills,
and intellectual resources is a critical
success factor in business (Davenport,
2004).
Afterstudentshavebecomeengaged
in thinking about thinking, profes
sors can implement the following two
experiential exercises and the following decision-tree analytical tool in the
classroom. They are good exercises in
management classrooms and training
programs that involve an overt effort
to improve metathinking processes.
Although in the classroom the discussion and analysis can become confusing,thedecision-treeframeworkallows
theprofessortopresenttheprocessina
straightforward manner. Thus, we recommend that the decision-tree be used
as a visual for framing the discussion.
However,itisinterestingtoallowclassroomparticipantstowanderthroughthe
process before providing a process for
structuringtheirthinking.
Example1:TheTaxicabAccident
Acabwasinvolvedinahit-and-runaccident at night. Two cab companies, the
Green and the Blue, operate in the city.
You are given the following data: (a)
85%ofthecabsinthecityareGreenand
15% are Blue and (b) a witness identifiedthecabasBlue.Thecourttestedthe
reliability of the witness under the same
circumstances that existed on the night
of the accident and concluded that the
witness correctly identified each one of
thetwocolors80%ofthetimeandfailed
20%ofthetime.
What is the probability that the cab
involved in the accident was Blue rather
than Green? Please write your answer
here:_____%
Most participants say that the probabilityisover50%thatthecabinvolved
in the accident was blue, and many
say that it is 80% (Tverksy & Kahneman, 1982).The later decision focuses
mainly on the conditional probability
that the witness accurately predicts a
cab’s color, when the color is known,
andignoresthebaseratemarginalprobabilityofcabsbeingblueversusgreen.
From a Bayesian analysis perspective,
the correct answer is 41%. Structuring
theprobleminadecisiontreeishelpful
insolvingtheproblem(seeFigure1).
Additionaldefensiblesolutionstothe
cab problem are found in the literature(e.g.,Birnbaum,1983;Levi,1983).
For example, according to Gigerenzer
(2000),
IfNeyman-Pearsontheoryisappliedtothe
cabproblem,solutionsrangebetween0.28
and0.82,dependingonthepsychological
theoryaboutthewitness’scriterionshift—
the shift from witness testimony at the
time of the accident to witness testimony
atthetimeofthecourt’stest.(p.16)
Thus,educatorscangobeyondTversky and Kahneman’s (1974) view of
one correct answer that Bayes’ statisticssuppliedandgobeyondconsidering
the deviation between the participant’s
answer and the so-called normative
answer as a bias of reasoning. Gigerenzer (2000, p. 17) quotes Neyman
and Pearson (1928) on this point: “In
many cases there is probably no single best solution” (p. 176). Because
of the nuances (contingencies) in how
the problem is framed, it is important
for professors to advocate a particular
theoretical model to follow in deciding on a final answer to the problem
(cf.Koehler,1993;Woodside&Singer,
1994) rather than advocating exactly
one correct solution. It is unfortunate
that, except in a statistics classroom,
extending the discussion beyond that
offeredbyTverskyandKahnemanmay
confuse students, causing them to lose
sight of the primary motivation for the
exercise: to think about their thinking.
In many classrooms, keeping the decision tree and subsequent discussion as
a Bayesian analysis may be the best
approachforteachingandlearning.
Example2:PricingaNewProduct
(AdaptedFromWoodside,1999)
UsingBayesiananalysisandthedecisiontreeasananalyticaltool,thetaxicabexamplefurtherengagesstudentsin
themetathinkingprocess.Thesimplicityoftheexampleeasesstudentsintothe
analyticalprocesswhileprovidingthem
withatoolforthinkingaboutthinking.
However, many business school situations are not as straightforward as the
taxicab example. There are generally
various viewpoints and functional perspectives in business decision making,
andallopinionsmustbeincludedinthe
decision-makingprocess.Thefollowing
example,thepricingofanewproduct,
presents a common business scenario
and allows students to dig deeper into
the use of a metathinking tool in the
decisionprocess:
Plaswireisanewfencingwiremadefrom
polyethylene terephthalate. Plaswire is
September/October2007
39
Baserate:
Marginal
Probabilities
Joint
Probabilities
Conditional
Probabilities
.80
Green
.68
Blue
.17
Green
.20
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.85
Decision:
Colorofcab
.20
.15
Green
.03
Blue
Blue
.12
.80
FIGURE1.Decisiontreefortaxicabcolorproblem.Theparticipantwillsay“blue”29%ofthetime(.17+.12=.29);the
participantwillbeaccurate41%ofthetimewhensaying“blue”(.12/.29=.41).Thus,whentheparticipantsays“blue,”
thechancesarestillgreaterthan50:50thatthecabwasgreen.
designedasareplacementforgalvanized
steel wire in permanent fencing construction.ThepresidentofKiwiFencing
requests that the assistant sales manager
implementapricingstrategyforPlaswire
thatwillhelpitachievenationaldistribution. The president believes that pricing
Plaswire substantially lower (30% less)
than the competing steel wire price will
help gain distributor acceptance of the
product, as some farmers and livestock
station managers may be price sensitive.
Thepresidentfeelscertainthatsteelwire
competitorswillnotrespondbylowering
prices in response to a low introductory
pricebecausethecostofgalvanizedsteel
raw material is 300% higher than the
plastic raw materials used for manufacturingPlaswire.
After reviewing industrial distributor
price lists for agricultural products, the
assistant sales manager knows that competitors tend to respond to such a competitive threat with price reductions that
matchorexceedthenewproduct’sprice.
When manufacturers introduced new
productsatretailpriceswellbelowcompeting products, competitors responded
withsimilarpricereductionsthreeoutof
40
JournalofEducationforBusiness
fourtimes.Thefinalresultwasfailureor
verylowmarketsharefornewproductsin
92ofthe96casesthattheassistantsales
managerhadreviewed.
However, the assistant sales manager
alsoknewthatthepresidentoftenpredicted
competitivereactioncorrectly.Thepresident
hadbeencorrectinhispredictionsintwoof
thethreerecentcasesconcerningcompetitors’ responses to new product prices.The
assistantsalesmanagerfavorspricingPlaswiretomatchthecurrentpriceofcompeting
steel wire products. This pricing decision
wouldresultinaquickpaybackperiodand
substantial profit, with the competitor less
likelytoreactwithaparitypricingstrategy.
The marketing manager recommends
pricing Plaswire 10% above the current
price of steel wire. She feels that few
agricultural customers will buy the new
permanent boundary wire because of its
low price and that the competitor will
loweritspricetomatchorbeatPlaswire’s
price.Themarketingmanagernotesthat,
in most cases of new-product introductionswithpriceshigherthancompetitors’
prices on existing products, only one in
10 competitors reacted by lowering the
priceonanexistingproduct.Inaddition,
thenewproductswerestillavailableeven
when introduced at prices higher than
competitors’prices.
What decision do you recommend?
Whatisthelikelihoodofsuccessofyour
strategy(i.e.,thenewproductisstillbeing
marketed 5 years after market introduction,anditisprofitable)?Pleaseprovide
youranswershere.
Yourrecommendation,circleone:
(a) Price Plaswire 30% below that of
steelwire.
(b) Price Plaswire equal to the current
priceofsteelwire.
(c)PricePlaswire10%abovesteelwire.
Thelikelihoodofsuccessofyourstrategy
is(circleone):
(a)15%
(b)25%
(c)45%
(d)75%
(e)100%
Solving the Plaswire pricing problem requires probabilities for alternatives that are not in the problem
description.Usingallreasonableesti-
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matesleadstothesamerecommendation: A price that is higher than that
of steel wire results in the highest
likelihood of success. Figure 2 and
Figure 3 include the probabilities in
the problem description and one set
of reasonable probabilities for the
other alternatives. Most students and
executivesadvocateadoptingthepresident’srecommendationtopricelower
than the competing steel wire. They
do so without considering the base
rate probability (0.75) that competitorsusuallyreactwhenanewproduct
isintroducedatapricelowerthanthat
oftheirproduct.
As with the taxicab example, we
recommend that the professor use the
decisiontreesasvisualsinhelpingstudentsorganizetheirthinking.Inreality,
the probability estimation process is
notcomplicated,althoughitappearsto
be so when thinking is not organized.
Learningtoorganizeone’sthoughtsis
critical to the success of teaching and
learningmetathinking.
ClassroomUse
Professorshaveusedtheseexercises
successfully in various business classrooms and seminars at several universities around the world. Starting the
discussion with the simple arithmetic
problem engages participants in the
topic of metathinking. Following the
arithmetic problem with the taxicab
example helps participants progress
into the probabilistic components of
decisionmaking.Themorecomplicated Plaswire example focuses attention
on the use of probabilities in decision
makingandprovidesparticipantswith
muchmoreinformationwithwhichto
makeadecision.
People’s minds tend to limit cognitive effort (Payne, Bettman, &
Johnson, 1993) and prefer to apply
intuitive problem-solving routines
evenwhengivenstrongevidencethat
these routines are not very accurate.
For example, in an executive MBA
programatTulaneUniversity(WoodBase
rates
.10
Steelwire
responds
.10
.99
Kiwihighprice
p=1.10
.65
.90
.50
Decision
Steelwire
doesnotrespond
.75
Steelwire
responds
Kiwilowprice
p=.70
.25
Steelwire
doesnotrespond
.001
Failure=.00
.000
Success=1.00
.585
Failure=.00
Payofffrom
expected
likelihood
ofsuccess
.000Total=.586
.015
Failure=.00
.000
.70
Success=1.00
.350
.30
Failure=.00
.97
Steelwire
doesnotrespond
Success=1.00
.03 Success=1.00
Steelwire
responds
Kiwiparityprice
p=1.00
.50
.35
side, 1997), the instructors tested the
Plaswire pricing case experimentally
among24two-persongroupsofexecutives.Theprofessorsinstructed12of
thegroupsintheuseofdecisiontrees
for framing problems and computing
expected values of alternative solutions.Theother12groupsreceivedno
such instruction. Of the 12 untrained
groups,8recommendedthelow-price
solution, and none recommended the
high-pricesolution.Ofthe12trained
groups, 7 decided on the high-price
solution,andonly2selectedthelowpricesolution.
Althoughtheprescriptivesolutionto
eachoftheexamplesisinformativeand
a necessary component of the classroom experience, the major objective
of the exercises is to engage students
intheartandscienceofunderstanding
one’s metacognitive abilities. Accordingly,theprofessorshoulddebriefparticipantsaftertheuseofeachexample.
The following questions would facilitatethedebriefing:
.000Total=.365
.04
Success=1.00
.030
.96
Failure=.00
.000
.75
Success=1.00
.25
Failure=.00
.1875
.000Total=.2175
FIGURE2.DecisiontreeofKiwiFencingpricealternatives.Forpayoff,survivalafterafewyearsisjudgedasuccess
andassignedavalueof1;deathisjudgedasfailureandassignedavalueof0.
September/October2007
41
CEO“certainty”
transformedinto
.99probabilitythat
competitorwillnot
respond
CEOaccurate?
Priorprobability
Yes
.66
Predictscompetitor
responds
.75
Yes
1.=.0050
.25
No
2.=.0016
Yes
.34
No
.01
CEO
predicts?
Yes
.99
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Revisedprobability
.66
Predictscompetitor
doesnotrespond
.34
No
.75
3.=.0026
.25
No
4.=.0008
.75
Yes
5.=.4752
.25
No
6.=.1584
.75
Yes
.25
No
7.=.2524
8.=.0841
FIGURE3.CEOpredictions,accuracy,andoutcomes.Posteriorprobabilitycompetitorresponds=.0050+.0026+.4752
+ .2524 = .74. Posterior probability competitor does not respond = .0016 + .0008 + .1584 + .0841 = .26. Conclusion:
BecausetheCEOisnothighlyaccurateandthepriorprobabilityisveryhighthatthecompetitorwillrespond(.75),the
posteriorlikelihoodofthecompetitorrespondingisclosetothesameasthepriorprobability.
1.What were your thoughts when
readingtheexample?
2.Howdidyouorganizeandusethe
informationprovidedintheexample?
3.What (if any) personal knowledge
didyouuseinarrivingatananswer?
Once participants dissect their own
thought processes, they grow more
awareoftheirownknowledge,assumptions, skills, and intellectual resources
and become cognizant of the way they
use these resources in decision making about marketing. Through close
attention to one’s thought processes,
participantstakethefirststepinacquiring, learning, and practicing skills in
metathinking.
The literature on metathinking is
robust and useful in providing tools
that help reduce problem ambiguity
andevaluateactionsandoutcomes.We
recommend the following reference
and reading materials: Baron (2000),
GigerenzerandSelten(2002),Gilovich
(1991), Weick (1995), Weick and Sutcliffe(2001),andWoodside(2003).
42
JournalofEducationforBusiness
Summary
Executive thinking differs from scientific thinking in at least three fundamental ways (cf. Kozak, 1996). First,
scientists (e.g., academic researchers)
get to choose their problem. In organizations, circumstances often thrust the
problems (and symptoms of problems)
on the executive (e.g., see Mintzberg,
1978). Second, scientists focus on a
limited number of problems at a time.
However, a vast number of potential
problemsandamyriadofpossiblepresentation problem frames (see Wilson,
McMurrian, & Woodside, 2001) confront executives. Third, scientists have
therelativeluxuryoftimetoexplorethe
problemathand.Executives,especially
CEOs,donot.
Unfortunately, a person’s natural
tendencyindecisionmakingincludes
(a) drawing conclusions on the basis
ofverylimitedinformation,(b)being
overconfident in the possibility that
one’sinitialconclusionsareaccurate,
(c)notlookingfordisconfirmingevi-
dence, (d) discounting disconfirming
evidence if it does appear, (e) being
hostile to the belief that using decision tools such as computer software
programs (see Gaither, 2002) results
in more accurate problem framing
than does trusting one’s own judgment, (f) not thinking outside the
box and considering all theoretically
possible—even if seemingly implausible—combinations of events and
their outcomes, and (g) implementing a decision on the basis of limited
consultation with knowledgeable colleaguesorexperts.
Research findings support the conclusion that a person’s designing and
applying decision tools and simple
heuristics lead to more accurate decisions than does decision making by
intuitivethinkingalone(Gaither,2002;
Gigerenzer&Selten,2002;Gigerenzer,
Todd,&ABCResearchGroup,1999).
Using experiential exercises such as
thosedescribedinthisarticlecanhelp
people build skills that improve their
decisionmaking.
NOTES
Dr. Victoria Crittenden’s research interests
areformulationandimplementationofmarketing
strategies,especiallyasrelatedtocross-functional
decision making. She is widely recognized as a
caseresearcher,casewriter,andcaseteacher.
Dr. Arch G. Woodside’s research interests are
decisionmaking,marketingstrategies,andtourism.
Correspondence concerning this article should
beaddressedtoDr.VictoriaCrittenden,ChairpersonofMBACoreFaculty,BostonCollege,Fulton
450, 140 CommonwealthAvenue, Chestnut Hill,
MA02467.
E–mail:crittend@bc.edu
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:01 11 January 2016
REFERENCES
Bargh, J. A., Gollwitzer, P. M., Lee-Chai, A.,
Barndollar, K., & Troetschel, R. (2001). The
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Birnbaum, M. H. (1983). Base rates in Bayesian
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Clemen,R.T.,&Reilly,T.(2001).Makinghard
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Davenport, D. B. (2004). Mega success requires
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will.Cambridge,MA:MITPress.
Weick, K. E. (1995). Sensemaking in organizations.ThousandOaks,CA:Sage.
Weick,K.E.,&Sutcliffe,K.M.(2001).Managingtheunexpected.NewYork:Jossey-Bass.
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Woodside, A. G. (1997). Applying alternative
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September/October2007
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In Arts Education Policy Review (AEPR), teachers, teacher educators, administrators, policymakers,
researchers, and others involved in arts education discuss difficult, often controversial policy issues
regarding K–12 education in the arts throughout the nation and the rest of the world. Focusing on
education in music, visual arts, theater, dance, and creative writing, the journal encourages varied
views and emphasizes analytical exploration. AEPR’s purpose is to present and explore many points
of view; it contains articles for and against different ideas, policies, and proposals for arts education.
Its overall purpose is to help readers think for themselves, rather than to tell them how they should
think.
Contributors should make sure that any submission is a policy article, complete with policy
recommendations about arts education from prekindergarten through twelfth grade. Articles about
college education should focus on teacher preparation for these grades or teacher retention in arts
education. AEPR intends to bring fresh analytical vigor to perennial and new policy issues in arts
education. AEPR presents analyses and recommendations focused on policy. The goal of any article
should not be description or celebration (although reports of successful programs could be part of
a policy article).
Any article focused on a program (or programs) should address why something works or does not
work, how it works, how it could work better, and most important, what various policymakers (from
teachers to legislators) can do about it. Many articles are rejected because they lack this element.
These orientations can be applied to many issues—from the structure and results of psychometric
research to the values climate that would support the arts as an educational basic. They can deal
with the relationships of teacher preparation to cultural development, the problems of curriculum
building, the particular challenges of teaching specific art forms, and the impact of political,
economic, cultural, artistic, and other climates on decision making for arts instruction.
AEPR does not promote individuals, institutions, methods, or products. It does not aim to repeat
commonplace ideas. Editors want articles that show originality, probe deeply, and take discussion
beyond common wisdom and familiar rhetoric. Articles that merely restate the importance of arts
education, call attention to the existence of issues long since addressed, or repeat standard
solutions cannot be considered.
Authors must prepare their manuscripts according to the The Chicago Manual of Style, 15th edition,
for all matters of style. All manuscripts require an abstract, preferably no longer than 120 words,
and 3–5 keywords to be used for indexing purposes. Keywords should capture the precise content of
the manuscript and should be found in the abstract. Authors
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Building Skills in Thinking: Toward a Pedagogy in
Metathinking
Victoria Crittenden & Arch G. Woodside
To cite this article: Victoria Crittenden & Arch G. Woodside (2007) Building Skills in Thinking:
Toward a Pedagogy in Metathinking, Journal of Education for Business, 83:1, 37-44, DOI:
10.3200/JOEB.83.1.37-44
To link to this article: http://dx.doi.org/10.3200/JOEB.83.1.37-44
Published online: 07 Aug 2010.
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Date: 11 January 2016, At: 23:01
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BuildingSkillsinThinking:Towarda
PedagogyinMetathinking
VICTORIACRITTENDEN
ARCHG.WOODSIDE
BOSTONCOLLEGE
CHESTNUTHILL,MASSACHUSETTS
ABSTRACT.Mostmanagersdonot
receiveformaltraininginmetathinking—
thatis,theyarenottrainedformallyin
thinkingaboutthinkingorinthinkingabout
deciding.Inthisarticle,theauthorsreview
thelackofeducationalfocusonmetathinkingandsuggestseveraltoolsforimproving
thedecision-makingprocessandforskill
buildinginmetathinking.Thetoolsinclude
twoexperientialexercisesthatfacilitate
learninginmetathinking.
Keywords:decisionanalysis,learning
theory,metathinking,pedagogy
Copyright©2007HeldrefPublications
T
he way chief executive officers
(CEOs) draw conclusions and
makedecisionscanhavedisastrousconsequences. For example, government
CEOs GeorgeW. Bush (United States),
Tony Blair (United Kingdom), John
Howard(Australia),andadditionalcoalition country CEOs concluded in early
2003 that Iraqi leaders had weapons
of mass destruction and were refusing
to disarm these weapons. Thus, these
governmentCEOsmadethedecisionto
declarewaronIraq.Inmid-2004(more
thanayearafterthewarwasdeclaredas
officiallywon),theCEOshadfoundno
weapons of mass destruction, close-tofull-blown civil war was raging in Iraq,
andworldterrorismhadincreased.Amid
all of this, the U.S. CEO reported that
therewereweaponsofmassdestruction.
At a more mundane level, consider
whyhighlysuccessfulfirmssuchasPolaroid and Lucent became failures. The
coverstoryoftheMay27,2002issueof
Fortunemagazinedescribed10bigmistakes as the primary reasons why companiesfail,withacriticalmistakebeing
that people presume that the future will
begoodonthebasisofhistoricalsuccesses rather than planning for unexpected
changes(Charan,Useem,&Harrington,
2002).WeickandSutcliffe(2001)made
thesameobservationintheiraptlytitled
monograph,ManagingtheUnexpected.
These examples point out the obvious:thatCEOs,middlelevelmanagers,
andtherestofusarepronetodrawing
inaccurateconclusionsandmakingbad
decisions. It is unfortunate that such
thinking (a) occurs frequently, (b) can
be very expensive, (c) often wrecks
localeconomies,and(d)cancausemassive layoffs, terror, or even death (for
reviewsonthispoint,seeBaron,2000;
Bazerman,1998;Gilovich,1991).Rather than shaking our heads or pointing
fingersatsomeoneelse’sbaddecisions,
peopleshouldidentifytoolstoimprove
the accuracy and quality of decisions
(cf.Martz&Shepherd,2003).Inother
words, what tools will help decision
makersbecomemorecognizantoftheir
decisionprocessesandoutcomes?With
the high frequency and seriousness of
baddecisionmaking,thecreation,testing, and teaching of tools to improve
thinking (i.e., increasing sensemaking
quality in becoming aware, acquiring
knowledge,interpretingdataandinformation,drawingconclusions,deciding,
and evaluating) are important in the
businessschoolclassroom(cf.Shadish,
Cook,&Levitton,1991;Weick,1995).
MarchandOlsen(1976)observed,
“Individuals and organizations make
sense of their experiences and modify behavior in terms of their interpretations” (p. 56). Weick (1995)
referred to this as sensemaking, and
he described sensemaking as “how
peoplegeneratethatwhichtheyinterpret”(p.13).
September/October2007
37
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Parry (2003) suggested that sensemakingisaprocessinwhichindividualsandgroupsinorganizationsorganize
their experiences about reality. As an
example,considerthequestion,“What’s
reallyhappening?”Thisquestiongenerallycontainsfoursubissues:
1.What actions being done now help
improvetheorganization’sperformance?
2.What actions are wasted motions
(i.e.,whatactionsarewetakingthatdo
notcontributebutdowasteourtime)?
3.What actions harm the organization’s performance (i.e., what actions
are counterproductive in the organization’sachievementofwhatreallyneeds
tobeaccomplished)?
4.Whatactionsarewenotdoingnow
butshouldwebedoingtoimprovethe
organization’sperformance?
However,afifthsubissuethattends
to be overlooked in this process is
more important. How does one go
abouttheprocessoffindingoutwhat
isreallyhappening?Animplicitmental model that decision makers often
useisoneinwhichthepersonbelieves
thatwhatcomestomindfirstisaccurate(Senge,1990).
Itseemsthattheprocessofinterpretation
is so reflexive and immediate that we
often overlook it. This, combined with
the widespread assumption that there is
butoneobjectivereality,iswhatmaylead
people to overlook the possibility that
others may be responding to a very different situation. (Gilovich, 1991, p. 117;
cf.Surowiecki,2004)
Thisfifthsubissuerequiresindividualstoengageinmetathinking.Leffand
Nevin(1990)describedmetathinkingas
thinkingandcreatingstrategiestoassist
one’sthinking.
In this article, we advocate that faculty,students,andexecutivesadoptthe
view that all decision makers need to
learn and practice metathinking skills.
Although social psychologists spend
considerable time and energy studyingthinkingprocesses,businessschool
academicianshavenotdoneathorough
joboftranslatingtheresearchintoclassroom experiences that engage students
inunderstandingthelinkbetweenmetathinkingandbusinesssuccess.
In the next section, we review severalkeystudiesonandtoolsformeta38
JournalofEducationforBusiness
thinkinginrelationtodecisionquality.
Then, we suggest a simple classroom
example that engages students in the
thinking-about-thinkingtopic.Weprovidetworigorousexperientialexercise
examples that are applicable in the
businessschoolclassroom.Inthenext
section, we discuss classroom use of
the exercises and offer a brief list of
recommended readings. We conclude
withalookatthedifferencesbetween
scientificandexecutivethinking.
ImprovingtheQualityof
Decisions
Traditionally, educators have not
included formal training in metathinking in the core (or even elective) business education curriculum. Few academic programs, including those in
executiveeducation,includecoursesin
thinking about thinking or in thinking
about deciding. Therefore, few businessstudentsparticipateincoursesthat
focusonacquiringprescriptivetoolsfor
improving the quality of thinking and
deciding(e.g.,Sterman,2001).
Two reasons may be responsible for
thislackofeducationalfocusandtraining.First,overconfidencebiasiswidespread: Most executives tend to rely
toooftenontheirunconsciouslydriven
automatic thoughts (Bargh, Gollwitzer,
Lee-Chai, Barndollar, & Troetschel,
2001; Wegner, 2002). There is a natural tendency to assume that intuitive
beliefsareaccurateandthatrelyingon
external heuristics (e.g., written checklists,explicitprotocols)isunnecessary.
People’s initial response is often one
ofdisbeliefandresentment,evenwhen
presented with hard evidence that formalexternalsearchingofrelevantinformation sources and the use of explicit
decision rules result in more accurate
decisions than does intuitive judgment
alone.Suchresentmentisattributableto
animplicitlossofauthoritytoevaluate
anddecide(e.g.,Gaither,2002).Second,
researchinthearea,asascientificfield
of study, is relatively new. Researchers recognized metathinking—unlike
related fields of study (e.g., biology,
sociology, psychology)—as a field of
formalresearchonlyrecently(e.g.,see
the landmark works by Baron, 2000;
Shadishetal.,1991;Weick,1995).
ToolsforImprovingThinking
Quality
Regarding the quality of decisions,
Gilovich(1991)stated,
Afundamentaldifficultywitheffective
policy evaluation is that we rarely get
to observe what would have happened
if the policy had not been put into
effect. Policies are not implemented as
controlled experiments, but as concerted actions. Not knowing what would
havehappenedunderadifferentpolicy
makes it enormously difficult to distinguish positive or negative outcomes
fromgoodorbadstrategies.Ifthebase
rate of success is high, even a dubious
strategycanbeseenaswise;ifthebase
rateislow,eventhewiseststrategycan
seemfoolish.(pp.41–42)
Severalusefultoolsarenowavailable
forimprovingsensemakingcapabilities
(e.g., Baron, 2000; Gigerenzer, 2000;
Green, 2002; Green & Armstrong,
2004).These tools include (a) estimatingwhatwouldhappenifapolicyhad
notbeenputintoeffect(e.g.,Campbell,
1969) and (b) software programs that
help structure problems and test the
impactofalternativeproblemstructures
(e.g.,Clemen&Reilly,2001).
Training in metathinking may help
overcome fundamental attribution error.
Fundamental attribution error is the
tendency of a person to blame other
peopleorenvironmentalforcesforabad
decisionratherthanrecognizingthatthe
processthatthepersonappliedtoreacha
decision reflects shallow systems thinking (Plous, 1993). “When we attribute
behavior to people rather than system
structure, the focus of management
becomes scapegoating rather than the
design of organizations [and problemsolving procedures] in which ordinary
peoplecanachieveextraordinaryresults”
(Sterman,2001,p.17).
The introduction and application of
variousmetathinkingtoolsintothetraditional school of management classroom may be difficult for educators to
implement,particularlygiventhefunctional nature of most business school
curricula. However, there is one metathinkingprocess,experientialexercises,
that fits clearly within the school of
thought referred to as constructivism
(Garman,1997;Martz,Neil,&Biscaccianti, 2003). Constructivism theories
suggestthatactiveparticipationisbene-
ficialinthelearningprocess.Inanexperientialexercise,learnerscanconstruct
a world by combining past informationwithfuture-orienteddispositionsto
actively engage in the learning process
(cf.Kolb,1984).
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Classroom-Oriented,Experiential
ExercisesinMetathinking
Thinking about how one thinks is
not an easy topic of discussion among
students,regardlessofwhethertheyare
undergraduates, graduates, or executives.Bynature,thetopicof“thinking”
is vague and hard to grasp. Davenport
(2004)suggestedabasic,yetprovoking,
exerciseforengagingpeopleinthinking
abouthowtheythink:
2+2=_____
Howdoyouarriveattheanswer?
A.Purelyfrommemory
B.Analysisofthenumbers
C.Counting
D.Avisualprocess
A natural response is, “Well, I just
know that!” This drives home the primary question, “How do you know
that?”Althoughsimplistic,thisexercise
engagesparticipantseasilyandactively
inadiscussionabouthowbusinesspeople address problems. In this scenario,
participants may recall kindergarten
classes with four of the same items on
the board, envision flash cards, recall
counting on fingers, or recall problem
recitation. Regardless of the solution
method,theexerciseforcesparticipants
to become aware of their individual
thinking processes. This awareness is
thefirststepinmetacognition.
This simple exercise and subsequent discussion engage students in
the metathinking process.This may be
the first time they—as business school
students—have talked about thinking,
althoughsomeoftheirsyllabi(particularly those in case-based courses) may
havereferredtocriticalthinkingskills.
Yet, becoming aware of one’s individual knowledge, assumptions, skills,
and intellectual resources is a critical
success factor in business (Davenport,
2004).
Afterstudentshavebecomeengaged
in thinking about thinking, profes
sors can implement the following two
experiential exercises and the following decision-tree analytical tool in the
classroom. They are good exercises in
management classrooms and training
programs that involve an overt effort
to improve metathinking processes.
Although in the classroom the discussion and analysis can become confusing,thedecision-treeframeworkallows
theprofessortopresenttheprocessina
straightforward manner. Thus, we recommend that the decision-tree be used
as a visual for framing the discussion.
However,itisinterestingtoallowclassroomparticipantstowanderthroughthe
process before providing a process for
structuringtheirthinking.
Example1:TheTaxicabAccident
Acabwasinvolvedinahit-and-runaccident at night. Two cab companies, the
Green and the Blue, operate in the city.
You are given the following data: (a)
85%ofthecabsinthecityareGreenand
15% are Blue and (b) a witness identifiedthecabasBlue.Thecourttestedthe
reliability of the witness under the same
circumstances that existed on the night
of the accident and concluded that the
witness correctly identified each one of
thetwocolors80%ofthetimeandfailed
20%ofthetime.
What is the probability that the cab
involved in the accident was Blue rather
than Green? Please write your answer
here:_____%
Most participants say that the probabilityisover50%thatthecabinvolved
in the accident was blue, and many
say that it is 80% (Tverksy & Kahneman, 1982).The later decision focuses
mainly on the conditional probability
that the witness accurately predicts a
cab’s color, when the color is known,
andignoresthebaseratemarginalprobabilityofcabsbeingblueversusgreen.
From a Bayesian analysis perspective,
the correct answer is 41%. Structuring
theprobleminadecisiontreeishelpful
insolvingtheproblem(seeFigure1).
Additionaldefensiblesolutionstothe
cab problem are found in the literature(e.g.,Birnbaum,1983;Levi,1983).
For example, according to Gigerenzer
(2000),
IfNeyman-Pearsontheoryisappliedtothe
cabproblem,solutionsrangebetween0.28
and0.82,dependingonthepsychological
theoryaboutthewitness’scriterionshift—
the shift from witness testimony at the
time of the accident to witness testimony
atthetimeofthecourt’stest.(p.16)
Thus,educatorscangobeyondTversky and Kahneman’s (1974) view of
one correct answer that Bayes’ statisticssuppliedandgobeyondconsidering
the deviation between the participant’s
answer and the so-called normative
answer as a bias of reasoning. Gigerenzer (2000, p. 17) quotes Neyman
and Pearson (1928) on this point: “In
many cases there is probably no single best solution” (p. 176). Because
of the nuances (contingencies) in how
the problem is framed, it is important
for professors to advocate a particular
theoretical model to follow in deciding on a final answer to the problem
(cf.Koehler,1993;Woodside&Singer,
1994) rather than advocating exactly
one correct solution. It is unfortunate
that, except in a statistics classroom,
extending the discussion beyond that
offeredbyTverskyandKahnemanmay
confuse students, causing them to lose
sight of the primary motivation for the
exercise: to think about their thinking.
In many classrooms, keeping the decision tree and subsequent discussion as
a Bayesian analysis may be the best
approachforteachingandlearning.
Example2:PricingaNewProduct
(AdaptedFromWoodside,1999)
UsingBayesiananalysisandthedecisiontreeasananalyticaltool,thetaxicabexamplefurtherengagesstudentsin
themetathinkingprocess.Thesimplicityoftheexampleeasesstudentsintothe
analyticalprocesswhileprovidingthem
withatoolforthinkingaboutthinking.
However, many business school situations are not as straightforward as the
taxicab example. There are generally
various viewpoints and functional perspectives in business decision making,
andallopinionsmustbeincludedinthe
decision-makingprocess.Thefollowing
example,thepricingofanewproduct,
presents a common business scenario
and allows students to dig deeper into
the use of a metathinking tool in the
decisionprocess:
Plaswireisanewfencingwiremadefrom
polyethylene terephthalate. Plaswire is
September/October2007
39
Baserate:
Marginal
Probabilities
Joint
Probabilities
Conditional
Probabilities
.80
Green
.68
Blue
.17
Green
.20
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:01 11 January 2016
.85
Decision:
Colorofcab
.20
.15
Green
.03
Blue
Blue
.12
.80
FIGURE1.Decisiontreefortaxicabcolorproblem.Theparticipantwillsay“blue”29%ofthetime(.17+.12=.29);the
participantwillbeaccurate41%ofthetimewhensaying“blue”(.12/.29=.41).Thus,whentheparticipantsays“blue,”
thechancesarestillgreaterthan50:50thatthecabwasgreen.
designedasareplacementforgalvanized
steel wire in permanent fencing construction.ThepresidentofKiwiFencing
requests that the assistant sales manager
implementapricingstrategyforPlaswire
thatwillhelpitachievenationaldistribution. The president believes that pricing
Plaswire substantially lower (30% less)
than the competing steel wire price will
help gain distributor acceptance of the
product, as some farmers and livestock
station managers may be price sensitive.
Thepresidentfeelscertainthatsteelwire
competitorswillnotrespondbylowering
prices in response to a low introductory
pricebecausethecostofgalvanizedsteel
raw material is 300% higher than the
plastic raw materials used for manufacturingPlaswire.
After reviewing industrial distributor
price lists for agricultural products, the
assistant sales manager knows that competitors tend to respond to such a competitive threat with price reductions that
matchorexceedthenewproduct’sprice.
When manufacturers introduced new
productsatretailpriceswellbelowcompeting products, competitors responded
withsimilarpricereductionsthreeoutof
40
JournalofEducationforBusiness
fourtimes.Thefinalresultwasfailureor
verylowmarketsharefornewproductsin
92ofthe96casesthattheassistantsales
managerhadreviewed.
However, the assistant sales manager
alsoknewthatthepresidentoftenpredicted
competitivereactioncorrectly.Thepresident
hadbeencorrectinhispredictionsintwoof
thethreerecentcasesconcerningcompetitors’ responses to new product prices.The
assistantsalesmanagerfavorspricingPlaswiretomatchthecurrentpriceofcompeting
steel wire products. This pricing decision
wouldresultinaquickpaybackperiodand
substantial profit, with the competitor less
likelytoreactwithaparitypricingstrategy.
The marketing manager recommends
pricing Plaswire 10% above the current
price of steel wire. She feels that few
agricultural customers will buy the new
permanent boundary wire because of its
low price and that the competitor will
loweritspricetomatchorbeatPlaswire’s
price.Themarketingmanagernotesthat,
in most cases of new-product introductionswithpriceshigherthancompetitors’
prices on existing products, only one in
10 competitors reacted by lowering the
priceonanexistingproduct.Inaddition,
thenewproductswerestillavailableeven
when introduced at prices higher than
competitors’prices.
What decision do you recommend?
Whatisthelikelihoodofsuccessofyour
strategy(i.e.,thenewproductisstillbeing
marketed 5 years after market introduction,anditisprofitable)?Pleaseprovide
youranswershere.
Yourrecommendation,circleone:
(a) Price Plaswire 30% below that of
steelwire.
(b) Price Plaswire equal to the current
priceofsteelwire.
(c)PricePlaswire10%abovesteelwire.
Thelikelihoodofsuccessofyourstrategy
is(circleone):
(a)15%
(b)25%
(c)45%
(d)75%
(e)100%
Solving the Plaswire pricing problem requires probabilities for alternatives that are not in the problem
description.Usingallreasonableesti-
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matesleadstothesamerecommendation: A price that is higher than that
of steel wire results in the highest
likelihood of success. Figure 2 and
Figure 3 include the probabilities in
the problem description and one set
of reasonable probabilities for the
other alternatives. Most students and
executivesadvocateadoptingthepresident’srecommendationtopricelower
than the competing steel wire. They
do so without considering the base
rate probability (0.75) that competitorsusuallyreactwhenanewproduct
isintroducedatapricelowerthanthat
oftheirproduct.
As with the taxicab example, we
recommend that the professor use the
decisiontreesasvisualsinhelpingstudentsorganizetheirthinking.Inreality,
the probability estimation process is
notcomplicated,althoughitappearsto
be so when thinking is not organized.
Learningtoorganizeone’sthoughtsis
critical to the success of teaching and
learningmetathinking.
ClassroomUse
Professorshaveusedtheseexercises
successfully in various business classrooms and seminars at several universities around the world. Starting the
discussion with the simple arithmetic
problem engages participants in the
topic of metathinking. Following the
arithmetic problem with the taxicab
example helps participants progress
into the probabilistic components of
decisionmaking.Themorecomplicated Plaswire example focuses attention
on the use of probabilities in decision
makingandprovidesparticipantswith
muchmoreinformationwithwhichto
makeadecision.
People’s minds tend to limit cognitive effort (Payne, Bettman, &
Johnson, 1993) and prefer to apply
intuitive problem-solving routines
evenwhengivenstrongevidencethat
these routines are not very accurate.
For example, in an executive MBA
programatTulaneUniversity(WoodBase
rates
.10
Steelwire
responds
.10
.99
Kiwihighprice
p=1.10
.65
.90
.50
Decision
Steelwire
doesnotrespond
.75
Steelwire
responds
Kiwilowprice
p=.70
.25
Steelwire
doesnotrespond
.001
Failure=.00
.000
Success=1.00
.585
Failure=.00
Payofffrom
expected
likelihood
ofsuccess
.000Total=.586
.015
Failure=.00
.000
.70
Success=1.00
.350
.30
Failure=.00
.97
Steelwire
doesnotrespond
Success=1.00
.03 Success=1.00
Steelwire
responds
Kiwiparityprice
p=1.00
.50
.35
side, 1997), the instructors tested the
Plaswire pricing case experimentally
among24two-persongroupsofexecutives.Theprofessorsinstructed12of
thegroupsintheuseofdecisiontrees
for framing problems and computing
expected values of alternative solutions.Theother12groupsreceivedno
such instruction. Of the 12 untrained
groups,8recommendedthelow-price
solution, and none recommended the
high-pricesolution.Ofthe12trained
groups, 7 decided on the high-price
solution,andonly2selectedthelowpricesolution.
Althoughtheprescriptivesolutionto
eachoftheexamplesisinformativeand
a necessary component of the classroom experience, the major objective
of the exercises is to engage students
intheartandscienceofunderstanding
one’s metacognitive abilities. Accordingly,theprofessorshoulddebriefparticipantsaftertheuseofeachexample.
The following questions would facilitatethedebriefing:
.000Total=.365
.04
Success=1.00
.030
.96
Failure=.00
.000
.75
Success=1.00
.25
Failure=.00
.1875
.000Total=.2175
FIGURE2.DecisiontreeofKiwiFencingpricealternatives.Forpayoff,survivalafterafewyearsisjudgedasuccess
andassignedavalueof1;deathisjudgedasfailureandassignedavalueof0.
September/October2007
41
CEO“certainty”
transformedinto
.99probabilitythat
competitorwillnot
respond
CEOaccurate?
Priorprobability
Yes
.66
Predictscompetitor
responds
.75
Yes
1.=.0050
.25
No
2.=.0016
Yes
.34
No
.01
CEO
predicts?
Yes
.99
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:01 11 January 2016
Revisedprobability
.66
Predictscompetitor
doesnotrespond
.34
No
.75
3.=.0026
.25
No
4.=.0008
.75
Yes
5.=.4752
.25
No
6.=.1584
.75
Yes
.25
No
7.=.2524
8.=.0841
FIGURE3.CEOpredictions,accuracy,andoutcomes.Posteriorprobabilitycompetitorresponds=.0050+.0026+.4752
+ .2524 = .74. Posterior probability competitor does not respond = .0016 + .0008 + .1584 + .0841 = .26. Conclusion:
BecausetheCEOisnothighlyaccurateandthepriorprobabilityisveryhighthatthecompetitorwillrespond(.75),the
posteriorlikelihoodofthecompetitorrespondingisclosetothesameasthepriorprobability.
1.What were your thoughts when
readingtheexample?
2.Howdidyouorganizeandusethe
informationprovidedintheexample?
3.What (if any) personal knowledge
didyouuseinarrivingatananswer?
Once participants dissect their own
thought processes, they grow more
awareoftheirownknowledge,assumptions, skills, and intellectual resources
and become cognizant of the way they
use these resources in decision making about marketing. Through close
attention to one’s thought processes,
participantstakethefirststepinacquiring, learning, and practicing skills in
metathinking.
The literature on metathinking is
robust and useful in providing tools
that help reduce problem ambiguity
andevaluateactionsandoutcomes.We
recommend the following reference
and reading materials: Baron (2000),
GigerenzerandSelten(2002),Gilovich
(1991), Weick (1995), Weick and Sutcliffe(2001),andWoodside(2003).
42
JournalofEducationforBusiness
Summary
Executive thinking differs from scientific thinking in at least three fundamental ways (cf. Kozak, 1996). First,
scientists (e.g., academic researchers)
get to choose their problem. In organizations, circumstances often thrust the
problems (and symptoms of problems)
on the executive (e.g., see Mintzberg,
1978). Second, scientists focus on a
limited number of problems at a time.
However, a vast number of potential
problemsandamyriadofpossiblepresentation problem frames (see Wilson,
McMurrian, & Woodside, 2001) confront executives. Third, scientists have
therelativeluxuryoftimetoexplorethe
problemathand.Executives,especially
CEOs,donot.
Unfortunately, a person’s natural
tendencyindecisionmakingincludes
(a) drawing conclusions on the basis
ofverylimitedinformation,(b)being
overconfident in the possibility that
one’sinitialconclusionsareaccurate,
(c)notlookingfordisconfirmingevi-
dence, (d) discounting disconfirming
evidence if it does appear, (e) being
hostile to the belief that using decision tools such as computer software
programs (see Gaither, 2002) results
in more accurate problem framing
than does trusting one’s own judgment, (f) not thinking outside the
box and considering all theoretically
possible—even if seemingly implausible—combinations of events and
their outcomes, and (g) implementing a decision on the basis of limited
consultation with knowledgeable colleaguesorexperts.
Research findings support the conclusion that a person’s designing and
applying decision tools and simple
heuristics lead to more accurate decisions than does decision making by
intuitivethinkingalone(Gaither,2002;
Gigerenzer&Selten,2002;Gigerenzer,
Todd,&ABCResearchGroup,1999).
Using experiential exercises such as
thosedescribedinthisarticlecanhelp
people build skills that improve their
decisionmaking.
NOTES
Dr. Victoria Crittenden’s research interests
areformulationandimplementationofmarketing
strategies,especiallyasrelatedtocross-functional
decision making. She is widely recognized as a
caseresearcher,casewriter,andcaseteacher.
Dr. Arch G. Woodside’s research interests are
decisionmaking,marketingstrategies,andtourism.
Correspondence concerning this article should
beaddressedtoDr.VictoriaCrittenden,ChairpersonofMBACoreFaculty,BostonCollege,Fulton
450, 140 CommonwealthAvenue, Chestnut Hill,
MA02467.
E–mail:crittend@bc.edu
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:01 11 January 2016
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In Arts Education Policy Review (AEPR), teachers, teacher educators, administrators, policymakers,
researchers, and others involved in arts education discuss difficult, often controversial policy issues
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Contributors should make sure that any submission is a policy article, complete with policy
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